Computational & AI-Assisted Methods for Social Sciences
Companion website to the research design primer by Ji Ma
This site carries everything the book produced and everything that has to stay current: computing setup, gentle primers on the techniques the exercises assume, living tool guides, the studies database, and worked notebooks with their data.
Everything here is free, requires no account, and lives at one address. If you are reading the book and hit a step that assumes knowledge you do not have yet, this is where to look first.
How the book and this site fit together
The book teaches through three complementary pieces, and it helps to know which one you are supposed to be in at any moment.
| What it does | Where it lives | |
|---|---|---|
| Theory chapters (1, 2, 3, 5, 7, 9, 11, 12) | Build the concepts and the reasoning: what a measure commits you to, what a method can and cannot claim, what makes a result trustworthy | The book |
| Exercise chapters (4, 6, 8, 10) | Put the same concepts to work on your own data, one deliverable at a time | The book |
| This site | Environment setup, technique primers, current tool lists, downloadable templates and datasets: the material that would be stale in print within a year | Here |
The book stays deliberately language-agnostic, so its exercises work in Python, R, or Stata. This site is more concrete: it shows one worked path (usually Python) so that you have something runnable in front of you while you adapt the idea to your own tools.
Start here
Getting started
Run your first analysis in the browser with no installation, or set up a local environment. Also covers getting access to an AI model.
Primers
Short, gentle walkthroughs of the three things the exercises assume: working with text data, supervised learning, and prompting.
Chapter materials
One hub per chapter: the primers it assumes, the notebooks and data it uses, the studies filtered to its lens.
The two ways in
By chapter. Chapter materials gives every chapter its own hub — what you will produce, what you need first, the worked notebook, the tool guide, and the studies database filtered to that chapter’s design stage. If you are working through the book, start here and you will not need to go looking.
By kind of material. The Library indexes the same things the other way round:
- CSS Empirical Studies Database — 122 published computational studies, annotated by design stage, searchable and downloadable.
- Worked notebooks and data — three notebooks with committed output, plus the sample network the Chapter 8 exercises run on.
- Primers — working with text data, supervised learning, and prompting and its failure modes.
- Tool guides — maintained versions of the “Useful Tools” lists that close each exercise chapter: data management, automated coding, analysis, and communication.
- Resource list — where to go beyond this book.
A note on how to use the AI material
Every AI-assisted technique in this book comes paired with a way to check it. That pairing is the point, not a hedge: an unverified model output is not evidence, however fluent it sounds. When a page here shows you how to have a model code text, draft code, or propose a schema, it will also show you how to audit what came back. Please do not skip that half.